Smoothness Priors Analysis of Time Series 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024

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Kitagawa, Genshiro/ Gersch, Will 作者

Springer Verlag

译者

1996-8 出版日期

290 页数

$ 168.37 价格

Pap

丛书系列

9780387948195 图书编码

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Smoothness Priors Analysis of Time Series 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

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Smoothness Priors Analysis of Time Series 在线电子书 pdf 下载 txt下载 epub 下载 mobi 在线电子书下载

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Smoothness Priors Analysis of Time Series addresses some of the problems of modeling stationary and nonstationary time series primarily from a Bayesian stochastic regression "smoothness priors" state space point of view. Prior distributions on model coefficients are parametrized by hyperparameters. Maximizing the likelihood of a small number of hyperparameters permits the robust modeling of a time series with relatively complex structure and a very large number of implicitly inferred parameters. The critical statistical ideas in smoothness priors are the likelihood of the Bayesian model and the use of likelihood as a measure of the goodness of fit of the model. The emphasis is on a general state space approach in which the recursive conditional distributions for prediction, filtering, and smoothing are realized using a variety of nonstandard methods including numerical integration, a Gaussian mixture distribution-two filter smoothing formula, and a Monte Carlo "particle-path tracing" method in which the distributions are approximated by many realizations. The methods are applicable for modeling time series with complex structures.

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